sss

classes/model/pix2code2.py

@@ -1,70 +0,0 @@
-__author__ = 'Ferdiand John Briones, attempt at pix2code2 through pretrained autoencoders'
-
-!pip install tensorflow
-import tensorflow
-from tensorflow import keras
-from keras.layers import Input, Dense, Dropout, RepeatVector, LSTM, concatenate, Flatten
-from keras.models import Sequential, Model
-from tensorflow.keras.optimizers import RMSprop
-from keras import *
-from .Config import *
-from .AModel import *
-from .autoencoder_image import *
-
-class pix2code2(AModel):
-	def __init__(self, input_shape, output_size, output_path):
-		AModel.__init__(self, input_shape, output_size, output_path)
-		self.name = "pix2code2"
-
-		visual_input = Input(shape=input_shape)
-
-		#Load the pre-trained autoencoder model
-		autoencoder_model = autoencoder_image(input_shape, input_shape, output_path)
-		autoencoder_model.load('autoencoder')
-		autoencoder_model.model.load_weights('../bin/autoencoder.h5')
-
-		#Get only the model up to the encoded part
-		hidden_layer_model_freeze = Model(inputs=autoencoder_model.model.input, outputs=autoencoder_model.model.get_layer('encoded_layer').output)
-		hidden_layer_input = hidden_layer_model_freeze(visual_input)
-		
-		#Additional layers before concatenation
-		hidden_layer_model = Flatten()(hidden_layer_input)
-		hidden_layer_model = Dense(1024, activation='relu')(hidden_layer_model)
-		hidden_layer_model = Dropout(0.3)(hidden_layer_model)
-		hidden_layer_model = Dense(1024, activation='relu')(hidden_layer_model)
-		hidden_layer_model = Dropout(0.3)(hidden_layer_model)
-		hidden_layer_result = RepeatVector(CONTEXT_LENGTH)(hidden_layer_model)
-
-		#Make sure the loaded hidden_layer_model_freeze will no longer be updated
-		for layer in hidden_layer_model_freeze.layers:
-			layer.trainable = False
-
-		#The same language model that of pix2code by Tony Beltramelli
-		language_model = Sequential()
-		language_model.add(LSTM(128, return_sequences=True, input_shape=(CONTEXT_LENGTH, output_size)))
-		language_model.add(LSTM(128, return_sequences=True))
-
-		textual_input = Input(shape=(CONTEXT_LENGTH, output_size))
-		encoded_text = language_model(textual_input)
-
-		decoder = concatenate([hidden_layer_result, encoded_text])
-
-		decoder = LSTM(512, return_sequences=True)(decoder)
-		decoder = LSTM(512, return_sequences=False)(decoder)
-		decoder = Dense(output_size, activation='softmax')(decoder)
-
-		self.model = Model(inputs=[visual_input, textual_input], outputs=decoder)
-
-		optimizer = RMSprop(lr=0.0001, clipvalue=1.0)
-		self.model.compile(loss='categorical_crossentropy', optimizer=optimizer)
-
-	def fit_generator(self, generator, steps_per_epoch):
-		self.model.summary()
-		self.model.fit_generator(generator, steps_per_epoch=steps_per_epoch, epochs=EPOCHS, verbose=1)
-		self.save()
-
-	def predict(self, image, partial_caption):
-		return self.model.predict([image, partial_caption], verbose=0)[0]
-
-	def predict_batch(self, images, partial_captions):
-		return self.model.predict([images, partial_captions], verbose=1)

main_program.py

@@ -5,7 +5,7 @@ import os.path
 from os.path import basename
 
 from classes.Sampler import *
-from classes.model.pix2code2 import *
+#from classes.model.pix2code2 import *
 
 
 def code_gen(path_to_input_image):